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Computational Cognitive Neuroscience
Shyh-Kang JengDepartment of Electrical Engineering/
Graduate Institute of Communication/
Graduate Institute of Networking and Multimedia
Artificial Intelligence
http://www.takanishi.mech.waseda.ac.jp/top/research/music/flute/wf_4rv/index_j.htm
http://www.research.ibm.com/deepblue/meet/html/d.1.shtmlhttp://www.research.ibm.com/deepblue/press/html/g.6.6.shtml
羅仁權 , 再造一個青年愛因斯坦 , 台大科學創造新文明特展 , 2011
Jeff Hawkins’s Comments on Artificial Intelligence AI defenders … a program that produces
outputs resembling (or surpassing) human performance on a task in some narrow but useful way really is just as good as the way our brains do it
…this kind of ends-justify-the-means interpretation of functionalism leads
AI researchers astray
J. Hawkins, On Intelligence, Times Books, 2004
Artificial Neural Networks
R. O. Duda, P. E. Harr, and D. G. Stork, Pattern Classification, 2nd ed., John Wiley & Sons, 2001
Jeff Hawkins’s Comments on Artificial Neural Networks Connectionists intuitively felt the brain wasn’t
a computer and that its secrets lie in how neurons behave when connected together
That was a good start, but the field barely moved on from its early successes
Research on cortically realistic networks was, and remains, rare
Jeff Hawkins’s Comments on Intelligence Since intelligence is an internal property of a
brain, we have to look inside the brain to understand what intelligence is
To succeed, we will need to crib heavily from nature’s engine of intelligence, the neocortex
No other roads will get us there
Cognitive Neuroscience To understand how neural processes give rise
to cognition Perception, attention, language, memory, problem
solving, planning, reasoning, coordination and execution of action
“Cognitive neuroscience – with its concern about perception, action, memory, language, and selective attention – will increasingly come to represent the central focus of all neurosciences in the twenty-first century.”
Experimental Methodologies fMRI and other imaging modalities
Neural basis of cognition in human Multi-electrode arrays
Record from many separate neurons at a time Insight into representation of information
http://paulbourke.net/oldstuff/eeg/eeg2.jpeg http://en.wikibooks.org/wiki/File:Sleep_EEG_Stage_1.jpg
http://www.csulb.edu/~cwallis/482/fmri/fmri.h2.gif
Other Major Research Methods Processes occurring in individuals with
disorders Helpful to understand the “normal” case Animal models are also often used
Conscious experience Subject to scientific scrutiny through observables Including verbal reports or other readout methods Brief interval of time or longer periods of time
Different Mechanistic Goals Some focus on partitioning the brain into
distinct modules with isolable functions Some try to find detailed characterization of
actual physical and chemical processes Some look for something more general
Not the details themselves that matter Principles that are embodied in these details are
more important
Two-Route Model for Reading
http://en.wikibooks.org/wiki/File:1_1_twoRouteModelInReading.JPG
Computational Cognitive Neuroscience Understanding how the brain embodies the
mind, using biologically based computational models made up of networks of neuron-like units
Intersection of many disciplines Neuroscience Cognitive psychology Computation
Computational Model for Reading
http://www.lps.uci.edu/~johnsonk/CLASSES/philpsych/brain.jpgRandall C. O’Reilly and Yuko Munakata, Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain, MIT Press, 2000
Randall C. O’Reilly and Yuko Munakata, Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain, MIT Press, 2000
Usefulness of Models Work through in detail of proposed modular
mechanism Lead to explicit predictions that can be compared for an
adequate account exploration of what postulates imply about resulting
behaviors
Course Outline1. Introduction and Overview
I. Basic Neural Computational Mechanisms
2. Individual Neurons
3. Networks of Neurons
4. Hebbian Model Learning
5. Error-Driven Task Learning
6. Combined Model and Task Learning
Course OutlineII. Large-Scale Brain Area Organization and
Cognitive Phenomena7. Large-Scale Brain Area Functional
Organization8. Perception and Attention9. Memory10. Language11. High-Level Cognition
Textbook and Website Randall C. O’Reilly and Yuko Munakata,
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain, MIT Press, 2000.
http://cc.ee.ntu.edu.tw/~skjeng/CCN2011.htm
Software Emergent For practicing examples in the textbook and
doing homeworks as well as the term project Enhanced from PDP++ Downloadable from
http://grey.colorado.edu/emergent/index.php/
Main_Page
http://grey.colorado.edu/emergent/index.php/File:Screenshot_ax_tutorial.png
References Thomas J. Anastasio, Tutorial on Neural
Systems Modeling, Sinauer Associates Inc. Publishers, 2010
Bernard J. Baars and Nicole M. Gage, Cognition, Brain, and Consciousness:Introduction to Cognitive Neuroscience, 2nd ed., Academic Press, 2010
References Friedemann Pulvermuller, The Neuroscience
of Language, Cambridge University Press, 2002
Douglas Medin, Brian H. Ross, Arthur B. Markman, Cognitive Psychology, 4th ed,. Wiley, 2004
References Patricia Churchland and Terrence J.
Sejnowski, The Computational Brain (Computational Neuroscience), MIT Press, 1994
Peter Dayan and L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, MIT Press, 2005
References J. Hawkins, On Intelligence, Times Books,
2004